Inverse problems in eddy current testing using neural network

2000 ◽  
Author(s):  
N. Yusa
Keyword(s):  
Neural Network ◽  
Inverse Problems ◽  
Eddy Current ◽  
Eddy Current Testing ◽  
Current Testing

scholarly journals Defect Image Recognition and Classification for Eddy Current Testing of Titanium Plate Based on Convolutional Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weiquan Deng ◽  
Jun Bao ◽  
Bo Ye
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Eddy Current ◽  
Structural Characteristics ◽  
Eddy Current Testing ◽  
Production Environment ◽  
Titanium Plate ◽  
Noise Interference ◽  
Current Testing

In the actual production environment, the eddy current imaging inspection of titanium plate defects is prone to scan shift, scale distortion, and noise interference in varying degrees, which leads to the defect false detection and even missed inspection. In view of this problem, a novel image recognition and classification method based on convolutional neural network (CNN) for eddy current detection of titanium plate defects is proposed. By constructing a variety of experimental conditions and collecting defect signals, the characteristics of eddy current testing (ECT) signals for titanium plate defects are analyzed, and then the convolution structure and learning parameters are set. The structural characteristics of local connectivity and shared weights of CNN have better feature learning and characterization capabilities for titanium plate defect images under scan shift, scale distortion, and strong noise interference. The results prove that, compared with other deep learning and classical machine learning methods, the CNN has a higher recognition and classification accuracy for the defect eddy current image of the titanium plate in the complex detection environment.

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